جست‌وجوی استاد و پژوهشگر

Abby Innesمشاهده پروفایل

دانشیار

• Political Economy
• Neoliberalism
• Soviet Political Economy
• +۵ مورد دیگر

Abby Innes is an Associate Professor of Political Economy at the European Institute, London School of Economics , where she has been a faculty member since 1997. Her academic career includes a Visiting Fellowship at MIT (1995–1997) and a Jean Monet Fellowship at the European University Institute (2001–2002) . She has received multiple teaching awards , including the LSE Teaching Prize and European Institute Departmental Teaching Prize, and currently serves as the Teaching Chair of her institute. Research Interests : Dr. Innes specializes in the political economy of Central Europe , neoliberalism , and materialist utopias . Her work explores state capture , systemic transitions , and comparative capitalism , with a focus on the UK’s neoliberal transformation. Recent research addresses the political economy of the green transition and neoliberal institutional failures . British Academy Mid-Career Fellowship (2017-2018) LSE Teaching Prize (2002) LSE/ASRC Excellence in Education Award (2020) European Institute Departmental Teaching Prize (2011, 2013, 2015) Nominee for LSE Student-Led Teaching Excellence Awards (2014, 2020) Publications : Her 2023 book Late Soviet Britain: Why Materialist Utopias Fail argues that UK neoliberalism parallels Soviet economic dogma, leading to institutional rigidity , democratic erosion , and governance crises . She has published in journals such as The Review of International Political Economy and Comparative Politics , alongside frequent contributions to LSE Blogs and The London Review of Books .

Yaojun Zhangمشاهده پروفایل

استادیار

• Biological Physics
• Biophysics
• Theoretical Physics
• +۳ مورد دیگر

Yaojun Zhang is an Assistant Professor in the Department of Physics & Astronomy and the Department of Biophysics at Johns Hopkins University. She earned her PhD in Physics from the University of California, San Diego (2015), followed by postdoctoral fellowships at the Princeton Center for Theoretical Science (2015-2018) and the Princeton Center for the Physics of Biological Function (2018-2021). Her research focuses on biological physics, particularly the complex behaviors of biomolecules and their assemblies across scales—from single-molecule folding to intracellular transport and biomolecular phase separation. She employs theoretical, mathematical, and computational tools to bridge biological questions with physical principles. Education PhD in Physics, University of California, San Diego (2015) Postdoctoral Fellowships: Princeton University (2015-2021) Research Interests Her group studies biomolecular condensates and liquid-liquid phase separation, exploring how microscopic interactions determine macroscopic properties of cellular compartments. Key areas include: Biomolecular condensate formation and dynamics Phase separation in cellular environments Interactions between biomolecules and cellular components Biophysics of intracellular transport Collaborations & Tools Zhang collaborates with experimentalists to validate theoretical models and develops frameworks for understanding condensate functions, such as surface tension, stoichiometry, and phase diagrams. Her work addresses challenges like condensate stability, molecular exclusion, and biological function regulation. Labs & Resources She leads the Zhang Lab , which integrates experimental and computational approaches. Her team’s research is supported by resources at the Bloomberg Center for Physics and Astronomy.

David Alvarez-Melisمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Optimal Transport
• +۴ مورد دیگر

David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۴ مورد دیگر

Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.

Jelani Nelsonمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Data Streams
• +۸ مورد دیگر

Jelani Nelson is a Professor and Department Chair in the UC Berkeley EECS Department (College of Engineering). His work focuses on theoretical computer science , particularly algorithms , data streams , dimensionality reduction , and privacy-preserving computation . Advising : Current students include Ishaq Aden-Ali, Xin Lyu, Mihir Singhal, and Hongxun Wu (co-advised with leading researchers). Education : PhD from MIT (George M. Sprowls Award), M.Eng from MIT. Research Highlights : Developed foundational results in Johnson-Lindenstrauss dimensionality reduction (optimality, sparse embeddings). Advancements in differential privacy (lower bounds, private mean estimation, threshold learning). Pioneering work on streaming algorithms for heavy hitters, norm estimation, and graph problems. Innovations in compressed sensing and oblivious subspace embeddings . Scientific Awards : PODS Best Paper Award (2011, 2022) IBM Pat Goldberg Memorial Best Paper Award (2011) George M. Sprowls Award for MIT doctoral thesis (2009) NeurIPS 2020 Spotlight Presentation

Henry Mossمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Optimization
• Theoretical Physics
• +۳ مورد دیگر

Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk

David Kerrمشاهده پروفایل

استاد

• Operator Algebras
• Ergodic Theory
• Mathematical Physics
• +۳ مورد دیگر

Prof. David Kerr is a Professor at the University of Münster's Department of Mathematics, within the Faculty of Mathematics and Computer Science. His research focuses on operator algebras, ergodic theory, and mathematical physics. He has contributed to projects such as the CRC 1442 on entropy and group dynamics, and has organized conferences including 'Group Actions: Dynamics, Measure, Topology' in 2022. His work spans topics like C*-algebras, sofic groups, and dynamical systems. Education: Not explicitly detailed in the text, but his career indicates advanced training in mathematics. Research Interests: Operator algebras, ergodic theory, entropy theory, and their applications to group actions and dynamical systems. Grants/Projects: Involved in CRC 1442 projects D04 and D05, exploring entropy, C*-algebras, and dynamical tilings. Labs/Teams: Collaborates with researchers like Hanfeng Li and Robin Tucker-Drob on projects involving operator algebras and group dynamics.

• Brain-computer interface
• Rehabilitation Robotics
• Machine Learning
• +۵ مورد دیگر

Dr. Yi-Feng Chen is a Research Assistant Professor and Master's Supervisor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech as a postdoctoral fellow in November 2020 and was promoted to Research Assistant Professor in February 2023. His academic journey includes interdisciplinary training across engineering, neuroscience, and biomedical applications. Dr. Chen's educational background includes: Ph.D. in Engineering from Wuhan University of Technology (2014-2017), supervised by Professor Quan Liu M.Sc. from Wuhan University of Technology (2011-2014), supervised by Professor Zhou Zude B.Sc. from Wuhan University of Technology (2007-2011) He also participated in exchange programs at Yuan Ze University in Taiwan (2012) and the University of Auckland in New Zealand (2015). Dr. Chen's research spans the intersection of biomedical engineering, neuroscience, and artificial intelligence, with particular focus on brain-computer interfaces and rehabilitation technologies. His work combines advanced signal processing techniques with clinical applications, especially in decoding neural signals for movement intention and monitoring brain states during anesthesia. His research has significant implications for neurorehabilitation, assistive technologies, and intraoperative neurophysiological monitoring. His recent publications demonstrate a clear trajectory toward increasingly sophisticated neural decoding techniques, with a focus on coordinated limb movements and practical rehabilitation applications. The work shows progression from basic EEG signal processing to complex bimanual movement decoding and robot-assisted rehabilitation systems, reflecting a translational research approach from basic science to clinical applications. Dr. Chen has secured significant research funding as principal investigator and core contributor on multiple projects: National Natural Science Foundation of China Youth Science Fund Project (2024-2026) Guangdong Natural Science Foundation General Project (2024-2026) Ministry of Science and Technology National Key R&D Program Project (2023-2026) Shenzhen Science and Technology Innovation Commission Key Project (2022-2025) As a Master's Supervisor, Dr. Chen mentors graduate students in biomedical engineering with focus on neural engineering and rehabilitation robotics. His laboratory collaborates closely with clinical partners to ensure research relevance to real-world medical challenges, particularly in neurorehabilitation and intraoperative monitoring.

Gary Pielakمشاهده پروفایل

استاد

• Protein Biophysics
• In-Cell NMR Spectroscopy
• Macromolecular Crowding
• +۳ مورد دیگر

Gary Pielak is a Kenan Distinguished Professor of Chemistry, Biochemistry, and Biophysics at the University of North Carolina at Chapel Hill, with a joint appointment in the School of Medicine. His research focuses on high-resolution protein NMR studies in living cells and the biophysics of tardigrade desiccation-tolerance proteins, bridging structural biology and molecular biophysics. Education: BS in Chemistry from Bradley University (1977), PhD in Biochemistry from Washington State University (1983), Postdoc at the University of British Columbia (1983-1986), Postdoc at Oxford University (1986-1988). Research Interests center on understanding protein structure, stability, and function in physiologically relevant environments. Key areas include: In-Cell NMR: Quantifying protein behavior in living cells using advanced NMR techniques. Macromolecular Crowding: Studying synthetic polymers and proteins as crowding agents to mimic cellular environments. Tardigrade Biology: Exploring desiccation-tolerance mechanisms in intrinsically disordered proteins from water bears. Recent Publications highlight interdisciplinary trends, combining AI-driven stability prediction, solid-state NMR for dry protein analysis, and molecular glass/gel applications for preservation. His Scientific Awards include: NIH Pioneer Award DuPont and Morrow Young Faculty Awards Multiple UNC Mentorship Awards Mentorship is a cornerstone, with a focus on training graduate students and advancing NMR methodologies. His group employs Research Methods : 19F, 1H, 15N, and 13C NMR Circular Dichroism and Calorimetry Protein Expression in E. coli

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +۱۱ مورد دیگر

Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

David Kutasovمشاهده پروفایل

استاد

• String Theory
• Quantum Field Theory
• Supersymmetry Breaking
• +۶ مورد دیگر

David Kutasov is a Professor in the Department of Physics at the University of Chicago, affiliated with the Enrico Fermi Institute. His research focuses on string theory and quantum field theory, particularly addressing dynamics of strongly coupled systems, supersymmetry breaking, black hole physics, and cosmological singularities. Kutasov has contributed to understanding the interplay between string theory and field theory, including mechanisms for vacuum selection in early universe scenarios and brane dynamics. His work explores theoretical frameworks such as holography, time-dependent backgrounds, and tachyon condensation, with applications to particle physics and cosmology. Key research directions include analyzing string theory's predictions for nature and applying string-based insights to experimental particle physics and cosmic phenomena. Notable contributions span topics like D-brane interactions, non-supersymmetric vacua, and dualities in Chern-Simons theories. Kutasov's publications often bridge abstract string theory constructs with observable phenomena, emphasizing tools for analyzing string theory's implications in diverse physical contexts. Despite extensive contributions, no specific scientific awards are explicitly listed in the provided materials. His research remains active across multiple frontiers of theoretical physics, maintaining a strong focus on foundational questions in high-energy physics.

Baharan Mirzasoleimanمشاهده پروفایل

استادیار

• Machine Learning
• Data Efficiency
• Robustness
• +۴ مورد دیگر

Baharan Mirzasoleiman is an Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), where she leads the BigML research group. Prior to joining UCLA, she was a postdoctoral research fellow in Computer Science at Stanford University working with Jure Leskovec. She received her Ph.D. in Computer Science from ETH Zurich advised by Andreas Krause. Her research focuses on addressing sustainability, reliability, and efficiency of machine learning, with particular emphasis on improving big data quality by developing theoretically rigorous methods to select the most beneficial data for efficient and robust learning. Her work spans several critical areas including data efficiency, robustness against label noise and data poisoning, and addressing spurious correlations in machine learning models. She has made significant contributions to understanding how neural networks exploit spurious features that correlate with certain categories during training but fail to generalize to minority groups. Professor Mirzasoleiman's research demonstrates how theoretically grounded approaches can lead to practical improvements in model robustness and efficiency across various applications including medical diagnosis and environmental sensing. Her work has resulted in the development of the SpuCo package, a Python library that provides modular implementations of state-of-the-art methods to address spurious correlations, along with controllable synthetic datasets like SpuCoMNIST and large-scale vision datasets like SpuCoAnimals. She has received numerous prestigious awards including the ETH medal for Outstanding Doctoral Thesis, being selected as a Rising Star in EECS by MIT, an NSF Career Award, a UCLA Hellman Fellows Award, and an Okawa Research Award. Her students have also received multiple fellowships and awards including Amazon Doctoral Student Fellowships and an OpenAI Superalignment Fast Grant. Professor Mirzasoleiman actively contributes to the academic community through invited talks at major conferences including ICML, ICLR, NeurIPS, and KDD, as well as co-organizing workshops on new frontiers in adversarial machine learning and sparsity in neural networks. She has developed educational resources including tutorials on Foundations of Data-efficient Learning presented at ICML 2024.

Jakob Foersterمشاهده پروفایل

دانشیار

• Multi-Agent Learning
• Deep Reinforcement Learning
• Human-AI Coordination
• +۵ مورد دیگر

Jakob Foerster is an Associate Professor at the University of Oxford's Department of Engineering Science and a Supernumerary Fellow at St Anne's College. He leads the FLAIR lab, focusing on multi-agent reinforcement learning (MARL), human-AI coordination, and AI foundational research. Previously, he was a Research Scientist at Facebook AI Research (FAIR) and holds a DPhil from Oxford. His work has been cited over 5,000 times and includes seminal contributions like QMIX and the Hanabi Challenge. Research interests span compute-efficient scaling of AI, MARL applications in finance and bio, and ethical AI. He actively collaborates across academia and industry, co-organizing workshops like NeurIPS' Emergent Communication. His lab emphasizes open-ended RL, environment design, and scalable algorithms. Notable awards include the CIFAR AI Chair (2019) and NeurIPS Best Paper Runner-Up (2018). Current efforts include FLAIR's research on zero-shot coordination and the JaxMARL framework. He advises students in Oxford's Engineering DPhil and AIMS CDT programs.

• Blockchain Technology
• Cryptocurrency
• Smart Contracts
• +۳ مورد دیگر

Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor at Cornell Tech (Cornell University) and a faculty member in the Department of Computer Science. He co-founded and co-directs the Initiative for CryptoCurrencies and Contracts (IC³) and serves as Chief Scientist at Chainlink Labs. His research focuses on blockchain technology, cryptocurrency, smart contracts, applied cryptography, authentication, and privacy. Juels holds a Ph.D. in Computer Science from UC Berkeley and previously held roles as Chief Scientist and Director of the RSA Laboratories at EMC/Dell EMC until 2013. His work spans foundational contributions to decentralized systems, including Chainlink’s oracle networks and Ekiden’s privacy-preserving smart contracts. Juels has authored influential books like *The Oracle* (2024) and *Tetraktys* (2009), blending cryptography with thriller narratives. He advises numerous students and alumni, many of whom have joined prominent blockchain firms like Chainlink Labs, Mysten Labs, and Espresso Systems. Recent publications explore topics such as liquidity in blockchain assets, oracle network security, and economic analysis of smart contracts. His research emphasizes practical applications of cryptographic principles to real-world challenges in digital systems and decentralized finance. Juels’ lab collaborates on projects like HyDRA (automated bug bounty systems) and Lanturn (economic security metrics). He actively participates in academic and industry partnerships, bridging theoretical computer science with applied blockchain innovation.

• Condensed Matter Physics
• High Energy Physics
• Quantum Information Science
• +۵ مورد دیگر

Thomas Faulkner is an Associate Professor in the Department of Physics at the University of Illinois at Urbana-Champaign, where he has been a faculty member since 2014. His research bridges condensed matter physics, high energy physics, and quantum information science through the framework of holographic duality (AdS/CFT correspondence), exploring connections between quantum field theories and gravitational theories. Dr. Faulkner received his BSc in Physics from the University of Melbourne in 2003 and his PhD from MIT in 2009 under Hong Liu and Krishna Rajagopal. He held postdoctoral positions at the Kavli Institute for Theoretical Physics (2009-2012) and the Institute for Advanced Study in Princeton (2012-2013) before joining the Illinois faculty. His primary research focuses on three interconnected areas: entanglement entropy as a tool to study quantum phases and gravity; string-inspired models of strongly correlated phenomena including non-Fermi liquids and quantum criticality; and holographic approaches to QCD under extreme conditions. His work leverages theoretical tools from both condensed matter and string theory communities to address fundamental questions in quantum gravity and many-body physics. Dr. Faulkner's publication record shows an evolving research trajectory from early work on strange metal transport and QCD applications toward increasingly sophisticated investigations of entanglement structure, quantum information aspects of holography, and fundamental constraints on quantum field theories. His recent work demonstrates deep connections between quantum information theory, gravitational physics, and condensed matter phenomena. DOE Early Career Award (2018) DARPA Young Faculty Award (2015) Dr. Faulkner has taught a comprehensive range of physics courses from undergraduate College Physics to advanced graduate-level field theory courses. His research program receives significant external funding, supporting his investigations into the quantum structure of spacetime and its connections to condensed matter phenomena. He participates in a vibrant research ecosystem exploring the quantum information foundations of spacetime geometry, contributing to collaborative efforts that are reshaping our understanding of the relationship between quantum mechanics and gravity.